更换文档检测模型

paddle_detection/ppdet/modeling/losses/supcontrast.py

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+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+import random
+from ppdet.core.workspace import register
+
+
+__all__ = ['SupContrast']
+
+
+@register
+class SupContrast(nn.Layer):
+    __shared__ = [
+        'num_classes'
+    ]
+    def __init__(self, num_classes=80, temperature=2.5, sample_num=4096, thresh=0.75):
+        super(SupContrast, self).__init__()
+        self.num_classes = num_classes
+        self.temperature = temperature
+        self.sample_num = sample_num
+        self.thresh = thresh
+    def forward(self, features, labels, scores):
+        
+        assert features.shape[0] == labels.shape[0] == scores.shape[0]
+        positive_mask = (labels < self.num_classes)
+        positive_features, positive_labels, positive_scores = features[positive_mask], labels[positive_mask], \
+                                                              scores[positive_mask]
+        
+        negative_mask = (labels == self.num_classes)
+        negative_features, negative_labels, negative_scores = features[negative_mask], labels[negative_mask], \
+                                                              scores[negative_mask]
+        
+        N = negative_features.shape[0]
+        S = self.sample_num - positive_mask.sum()   
+        index = paddle.to_tensor(random.sample(range(N), int(S)), dtype='int32')
+
+        negative_features = paddle.index_select(x=negative_features, index=index, axis=0)
+        negative_labels = paddle.index_select(x=negative_labels, index=index, axis=0)
+        negative_scores = paddle.index_select(x=negative_scores, index=index, axis=0)
+        
+        features = paddle.concat([positive_features, negative_features], 0)
+        labels = paddle.concat([positive_labels, negative_labels], 0)
+        scores = paddle.concat([positive_scores, negative_scores], 0)
+
+        if len(labels.shape) == 1:
+            labels = labels.reshape([-1, 1])
+        label_mask = paddle.equal(labels, labels.T).detach()
+        similarity = (paddle.matmul(features, features.T) / self.temperature)
+
+        sim_row_max = paddle.max(similarity, axis=1, keepdim=True)
+        similarity = similarity - sim_row_max
+
+        logits_mask = paddle.ones_like(similarity).detach()
+        logits_mask.fill_diagonal_(0)
+
+        exp_sim = paddle.exp(similarity) * logits_mask
+        log_prob = similarity - paddle.log(exp_sim.sum(axis=1, keepdim=True))
+
+        per_label_log_prob = (log_prob * logits_mask * label_mask).sum(1) / label_mask.sum(1)
+        keep = scores > self.thresh
+        per_label_log_prob = per_label_log_prob[keep]
+        loss = -per_label_log_prob
+
+        return loss.mean()